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Lack of skills is arguably one of the most important determinants of high levels of unemployment and poverty. In response, policymakers often initiate vocational training programs in effort to enhance skill formation among the youth. Using a regression-discontinuity design, we examine a large youth training intervention in Nepal. We find, twelve months after the start of the training program, that the intervention generated an increase in non-farm employment of 10 percentage points (ITT estimates) and up to 31 percentage points for program compliers (LATE estimates). We also detect sizeable gains in monthly earnings. Women who start self-employment activities inside their homes largely drive these impacts. We argue that low baseline educational levels and non-farm employment levels and Nepals social and cultural norms towards women drive our large program impacts. Our results suggest that the program enables otherwise underemployed women to earn an income while staying at home - close to household errands and in line with the socio-cultural norms that prevent them from taking up employment outside the house.
Low inflation was once a welcome to both policy makers and the public. However, Japans experience during the 1990s changed the consensus view on price of economists and central banks around the world. Facing deflation and zero interest bound at the s
The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserv
We propose a new algorithm for estimating treatment effects in contexts where the exogenous variation comes from aggregate time-series shocks. Our estimator combines data-driven unit-level weights with a time-series model. We use the unit weights to
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identificatio